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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, D16312, doi:10.1029/2006JD007178, 2006
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Secondary organic aerosols formed from oxidation of biogenic volatile
organic compounds in the Sierra Nevada Mountains of California
Thomas M. Cahill,1,2 Vincent Y. Seaman,1 M. Judith Charles,3 Rupert Holzinger,4
and Allen H. Goldstein4
Received 7 February 2006; revised 14 April 2006; accepted 17 May 2006; published 25 August 2006.
[1] Biogenic volatile organic compound (BVOC) emissions, such as isoprene and
terpenes, can be oxidized to form less volatile carbonyls, acids, and multifunctional
oxygenated products that may condense to form secondary organic aerosols (SOA). This
research was designed to assess the contribution of oxidized BVOC emissions to SOA in
coniferous forests by collecting high-volume particulate samples for 6 days and 5 nights
in the summer of 2003. The samples were analyzed for acids, carbonyls, polyols and
alkanes to quantify oxidized BVOCs. Terpene and isoprene oxidation products were
among the most abundant chemical species detected with the exception of hexadecanoic
acid, octadecanoic acid and two butyl esters of unknown origin. The terpene oxidation
products of pinonic acid, pinic acid, nopinone and pinonaldehyde showed clear diurnal
cycles with concentrations two- to eight-fold higher at night. These cycles resulted from
the diurnal cycles in gaseous terpene concentrations and lower temperatures that enhanced
condensation of semivolatile chemicals onto aerosols. The terpene-derived compounds
averaged 157 ± 118 ng/m3 of particulate organic matter while the isoprene oxidation
compounds, namely the 2-methyltetrols and 2-methylglyceric acid, accounted for 53 ±
19 ng/m3. Together, the terpene and isoprene oxidation products represented 36.9% of the
identified organic mass of 490 ± 95 ng/m3. PM10 organic matter loadings in the region
were approximately 2.1 ± 1.2 mg/m3, so about 23% of the organic matter was identified
and at least 8.6% was oxidized BVOCs. The BVOC oxidation products we measured were
significant, but not dominant, contributors to the regional SOA only 75 km downwind of
the Sacramento urban area.
Citation: Cahill, T. M., V. Y. Seaman, M. J. Charles, R. Holzinger, and A. H. Goldstein (2006), Secondary organic aerosols formed
from oxidation of biogenic volatile organic compounds in the Sierra Nevada Mountains of California, J. Geophys. Res., 111, D16312,
doi:10.1029/2006JD007178.
1. Introduction
[2] Aerosols significantly influence the radiative budget
of Earth’s atmosphere and impact visibility by scattering and
absorbing radiation and serving as cloud condensation
nuclei. Organic matter typically composes 20– 50% of the
aerosol mass below 2.5 mm diameter [e.g., North American
Research Strategy for Tropospheric Ozone, 2003], which
includes organic matter that is directly emitted to the
atmosphere as well as material formed through gas to
particle conversion resulting in secondary organic aerosol
(SOA) formation. The organic component of SOA may be
derived from either biogenic or anthropogenic precursors,
thus it is important to assess the contributions of both sources
1
Department of Environmental Toxicology, University of California,
Davis, California, USA.
2
Now at Department of Integrated Natural Sciences, Arizona State
University at West Campus, Phoenix, Arizona, USA.
3
Deceased October 2004.
4
Department of Environmental Science, Policy and Management,
University of California, Berkeley, California, USA.
Copyright 2006 by the American Geophysical Union.
0148-0227/06/2006JD007178$09.00
to understand SOA formation. While global budgets remain
highly uncertain, current estimates suggest biogenic SOA
sources (8 to 40 Tg yr 1) are much larger than anthropogenic
sources (0.3 to 1.8 Tg yr 1) [Intergovernmental Panel on
Climate Change (IPCC), 2001].
[3] Identification of the source of SOA relies on compound-specific measurements of the organic constituents of
organic aerosols. Many hundreds of organic compounds
have been identified in aerosol samples including alkanes,
substituted phenols, alkanals, sugar derivatives, aromatic
polycyclic hydrocarbons, monocarboxylic and dicarboxylic
acids [Fine et al., 2001; Nolte et al., 1999; Rogge et al.,
1993, 1997, 1998]. Often the objective of organic analysis
of aerosols is to identify tracers of aerosol sources, but these
analyses frequently focus on primary anthropogenic sources
rather than biogenic sources. Additionally, the compounds
which have been identified generally compose only a
fraction of the total organic mass present.
[4] Terrestrial vegetation emits a variety of reactive biogenic volatile organic compounds (BVOCs) including isoprene, alcohols, ketones, monoterpenes, and sesquiterpenes.
BVOC emissions are estimated to be approximately a factor
of 10 larger than anthropogenic VOC emissions globally
[Seinfeld and Pandis, 1998]. The BVOCs may be oxidized
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in the atmosphere to form less volatile compounds that may
condense onto aerosols and contribute to SOA formation.
Terpene and isoprene oxidation products have been observed
in aerosols in laboratory oxidation studies and in many
forested regions [Claeys et al., 2004a, 2004b; Kavouras et
al., 1998, 1999a, 1999b; Kavouras and Stephanou, 2002a,
2002b; Spanke et al., 2001; Yu et al., 1999].
[5] The objective of this research was to assess the
relative importance of biogenic emissions to organic aerosol
composition by collecting particulate samples and analyzing
them for a series of organic acids, carbonyls, sugars, polyols
and alkanes. In particular, we focused on terpene oxidation
products including pinic acid, pinonic acid, norpinic acid,
nopinone, and pinonaldehyde, and isoprene oxidation products including 2-methyltetrols, 2-methylglyceric acid and
pentenetriols. Measurements were conducted at the Blodgett
Forest AmeriFlux site where emissions of BVOCs and their
oxidation products have been studied in detail [Goldstein et
al., 2004; Holzinger et al., 2005; Lee et al., 2005; Schade
and Goldstein, 2003; Schade et al., 1999], and fine particle
growth events are frequently observed during the day
[Lunden et al., 2006]. Separate day and night aerosol
samples were collected to test for diurnal variation in
organic composition which might provide clues to their
sources. Gas-phase VOC and ozone concentrations were
also monitored to quantify the variation in precursors to the
observed biogenic oxidation products.
2. Materials and Methods
[6] Particulate matter was collected at the Blodgett Forest
AmeriFlux site from 9 to 14 September 2003. This site is a
secondary-growth Ponderosa pine (Pinus ponderosa) plantation located in the Sierra Nevada Mountains of California
at an elevation of 1320 m. The region is typical of mixed
temperate coniferous forests consisting of Pinus and Abies.
Blodgett Forest is located approximately 75 km downwind
of Sacramento, which has a population of nearly 1 million
people. The typical meteorology consists of a moderate
upslope wind (2.5 to 3 m/s) during the day, which transports
pollutants from Sacramento, and a weak downslope flow
(1.5 m/s) during the night, bringing less polluted air from
aloft to the site. The meteorology, gas phase VOC, ozone,
carbon dioxide, and humidity were monitored continuously
during the study period. Blodgett Forest has been extensively characterized with respect to meteorology and gaseous biogenic emissions and their photochemistry
[Goldstein et al., 2000; Holzinger et al., 2005; Lee et al.,
2005], thus it provided an ideal setting to study the
oxidation products of BVOCs in aerosols.
[7] The gas-phase concentrations of monoterpenes, nopinone, pinonaldehyde, pinonic acid, and methacrolein/
methyl vinyl ketone/crotonaldehyde, were monitored in situ
using a PTR-MS as has been previously reported [Holzinger
et al., 2005; Lee et al., 2005]. During each hour, air was
sampled through 6 individual gas inlets, each of which were
protected by a Teflon filter (PFA holder, PTFE membrane,
pore size 2 mm). One inlet was used to sample air at 12.5 m
from 0 to 30 min for eddy covariance flux measurements of
total monoterpenes. Five inlets were used to sample vertical
gradients at height levels within (1.1 m, 3.1 m, 4.9 m) and
above (8.75 m, 12.5 m) the forest canopy, which was
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approximately 6.5 m high, sequentially for 6 min each
during the second 30 min of each hour. The 5 gradient
inlets were identically designed; each consisted of 30 m
PFA tubing (ID 4 mm) protected by a Teflon filter and a
sample flow of 1 L/min was maintained at all times through
each inlet. Since the particulate matter was sampled at a
height of 3 m, only data from the 3.1 m inlet were utilized
for comparisons to aerosol data. The PTR-MS instrument
was operated under standard conditions (a pressure of
2.1 mbar in the reaction chamber and an electrical field of
66 V/cm), typical primary ion signals were 3 – 5 million
counts per second; see Holzinger et al. [2005] for details.
[8] Particulate matter was collected onto 20.3 25.4 cm
Tissuquartztm filters (PALL Life Sciences, Ann Arbor,
Michigan) by an Andersen High-volume total suspended
particulate sampler (model GBM2000H, Andersen Instruments Inc., Georgia, USA). The sampler was modified by
creating a 3 m high ‘‘snorkel’’ from 10.2 cm ID diameter
aluminum ducting in an effort to sample forest canopy air
more than ground-level air. The flow rate of the sampler was
set to 30 m3/h for all samples. Separate samples were
collected during the day (0830 to 2030 local time (LT))
and night (2200 to 0800 LT) to test for diurnal cycles in
particulate concentrations. A total of 6 day and 5 night
samples were collected sequentially during the study. The
quartz filters were baked at 800C for 8 hours prior to use to
remove organic contaminants. Two field blanks were prepared by randomly selecting two filters from the same batch
used in sampling, mounting them in the sampler but not
turning the vacuum pumps on, removing them from the
sampler, and storing them with the field samples. An
additional filter was used as a laboratory blank that did
not undergo transport and handling in the field.
[9] After sample collection, the filters were cut in half.
The first half of the filter was extracted by a Soxhlet
apparatus while the second half was archived. Prior to
extraction, butanedioic-13C2 acid (Cambridge Isotope Laboratories, Inc. Andover, MA), dodecanoic-13C1 acid, glucose-d2, 6-fluorochromanone (Sigma-Aldrich chemical Co.,
Milwaukee, Wisconsin), and pinonaldehyde-13C1 (synthesized at University of California, Davis) were added to the
filter to determine extraction efficiencies of the chemicals
from the actual field samples. These spiking chemicals were
dissolved in methanol and then blotted onto the filter in
several locations and then allowed to air dry to remove the
methanol solvent. In addition, three blank filters were used
for spike-recovery trials to determine extraction efficiencies
of a wider range of chemicals. The acids were represented
by pentanoic, decanoic, pentadecanoic, pentanedioic, cispinonic, pinic, and trans-norpinic acids (Sigma-Aldrich
Chemical Co.). The carbonyls were represented by
2-decanone, 2-indanone, nonanal, 3,5-heptanedione and unlabeled pinonaldehyde (Sigma-Aldrich Chemical Co.). The
filters were Soxhlet-extracted by 50:50 acetonitrile (ACN,
Optima grade, Fisher Scientific, Pittsburgh, Pennsylvania)
and dichloromethane (DCM, B&J CG2 grade, Honeywell
International, Inc., Muskegon, Michigan) for 24 hours
followed by an additional 24 hour extraction with methanol
(Purge and Trap grade, Fisher Scientific). The extracts
where then concentrated by roto-evaporation to 3 ml
and transferred to a graduated centrifuge tube, along with
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two rinses of the round bottom flask, for nitrogen evaporation to a final volume of 2 ml.
[10] The study investigated five classes of compounds
that required different derivatization methods. The first class
of chemicals was the acids, which included 35 target
analytes. The acids were derivatized by adding N,O-bis
(trimethylsilyl)trifluoroacetamide (BSTFA, Supelco, Bellefonte, Pennsylvania) as a silylation reagent. In this case,
aliquots of the ACN:DCM and methanol extracts were
combined and evaporated to dryness and then reconstituted
into 200 ml DCM. The internal standards of hexanoic-d11
and benzoic-d5 acids (Sigma-Aldrich Chemical Co.) were
then added to the samples. Twenty microliters of BSTFA:
chlorotrimethylsilane (10:1 v/v) were added to the samples
which were then heated at 45C for 24 hours. The samples
were then analyzed without further cleanup. The second
class of chemicals studied was the carbonyls. Only the
ACN:DCM extract was used since it contained the less
polar carbonyls while avoiding the more polar interferences.
In this case, benzaldehyde-d6 (Cambridge Isotope Laboratories) was used an internal standard. The carbonyls were
derivatized by adding 20 ml of a 50 mM pentafluorobenzylhydroxylamine (PFBHA, Sigma-Aldrich Chemical Co.)
solution to a 200 ml aliquot of the sample extract. The
samples were then allowed to react for 24 hours at room
temperature (22C) after which they were ready for analysis. The third group of chemicals investigated were the
sugars and sugar-derived compounds (8 compounds), which
were also derivatized by BSTFA, but the derivatization was
more aggressive. Aliquots of the ACN:DCM and methanol
extracts were combined and evaporated to dryness. The
samples were reconstituted and derivatized in a 50:50
solution of BSTFA and pyridine (Sigma-Aldrich Chemical
Co.), which is similar to previously used approaches
[Pashynska et al., 2002]. The samples were heated at
45C for 24 hours and then injected on the instrument.
The fourth group of chemicals was the polyols, which
consisted of the 2-methyltetrols, 2-methylglyceric acid,
and three pentenetriols. The 2-methyltetrols and 2-methylglyceric acid are isoprene oxidation products [Claeys et al.,
2004a]. The standards for the two methyltetrols were
obtained from Dr. Claeys who synthesized them. These
chemicals were derivatized in an identical fashion as the
sugars with a 50:50 mix of BSTFA and pyridine. The
last group was the ‘‘native’’ analysis, which consisted of
21 n-alkanes that required no derivatization. In this case, the
sample extract was added to a GC vial along with the
internal standards of tetracosane-d50, and hexatriacontaned74 (Cambridge Isotope Laboratories).
[11] In addition to the filter samples, surface soil, Ponderosa pine needle and Ponderosa pine wood samples were
collected from Blodgett forest to try to identify the source of
two unexpected nonpolar analytes, namely the butyl ester of
hexadecanoic acid and the butyl ester of octadecanoic acid.
Two aliquots (105 mg and 210 mg, wet weight) of the
surface soil sample were added to a centrifuge tube along
with sodium sulfate to dry the soil. The samples were
shaken three times with 3 ml of hexane each time and the
extract was evaporated to 200 ml. The pine needles and
wood were simply shaken in hexane to give a qualitative
presence/absence of the butyl esters in the samples. In
addition, two 2 cm2 patches from an archived filter, one
D16312
from the edge and one from the center of the filter, were also
extracted by shaking them in hexane to ensure that butyl
esters were not formed as a result of the Soxhlet extraction
procedure.
[12] All analyses were conducted by a Varian 3400 gas
chromatograph coupled to a Saturn 2000 ion trap mass
spectrometer (Varian Inc. Walnut Creek, California). Five
microliters of the samples were injected into an injection
port packed with silanized glass wool that was maintained at
5C below the boiling point of the sample solvent. The
injection port was then heated to 275C to volatilize the
chemicals from the injector port onto the analytical column.
Chromatographic separation was conducted with a DBXLBMSD column (5% phenyl substituted, 30 m length,
0.25 mm ID, 0.25 mm film thickness, Agilent Technologies,
Wilmington, Delaware) using helium as a carrier gas at
37 cm/s. The initial column temperature was 35C for the
acid and sugar analyses while it was 64C for the native and
carbonyl analyses. The column was then heated at 5C/min
to 330C where it was held for 8 minutes. The ion trap mass
spectrometer conditions were a 270C transfer line temperature and a 220C trap temperature. Electron ionization (EI)
was used for sample detection and quantification while
methane chemical ionization (CI) was used for analyte
confirmation as well as structural elucidation of unknown
chemicals. The EI and CI analyzes used were the Varian
default parameters except that the target ion count in EI was
reduced to 10,000 from 20,000 to improve the spectra in
dirty matrices.
[13] Quantification was conducted using a 6-point calibration curve which was obtained by derivatizing standard mixtures alongside the samples for each of the
chemical classes investigated. If a field sample exceeded
the calibration curve, then a new aliquot of the sample
extract was diluted and derivatized along with a new
calibration curve. The limit of detection was defined as
the field blank plus three standard deviations of the field
blank (blank + 3SD) while the limit of quantification
was defined as the field blank plus six standard deviations of the blank (blank + 6SD). All concentration
results were rounded to two significant digits of accuracy
after all calculations were completed.
3. Results
3.1. Gaseous Results
[14] The meteorological and ozone data showed diurnal
cycles during the sampling period that are typical for this
site and time of year for the western slope of the Sierra
Nevada Mountains (Figure 1). The ambient temperature
data showed a clear daily cycle with an average daytime
temperature of 21.3C and an average nighttime temperature of 12.9C during the sampling period. The wind speed
also showed a diurnal cycle with the highest wind speed
occurring during the daytime. The prevailing daytime
‘‘upslope’’ wind may also bring anthropogenic contaminants from the urban areas in the Central Valley. The
concentration of ozone was slightly higher during the day
(average = 57.0 ± 11.7 ppbv, n = 144) compared to the night
(avg = 45.6 ± 10.2 ppbv, n = 120). Therefore we would not
expect more than a factor of 2 differences in the rate of
BVOC oxidation product formation based on varying con-
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pinonaldehyde was detected, its concentrations were in the
29 to 175 ng/m3 (0.050 to 0.30 ppbv) range. Pinonic acid
was not detected in the gas phase during this study. The
observation that the less volatile compounds of pinonaldehyde and pinonic acid were either not detected or present at
low concentrations in the gas phase was not surprising since
these chemicals were expected to condense onto particulate
matter and thus not be present in the gas phase to any
considerable degree. Lastly, benzene was also monitored to
give an indication of influence from anthropogenic sources
from the Central Valley. The concentrations of benzene
were highest during the day, which corresponded to the
upslope airflow (Figure 2).
3.2. Particulate Results
[16] The results from the spike-recovery trials and labeled
surrogate compounds added to the field samples showed
that the extraction method was generally good (between 70
and 120%) for most of the chemicals investigated (Table 1).
For the acids, the exceptions were pentanoic acid that
evaporated during the drying step and decanoic acid which
seemed to suffer from contamination. Butanedioic-13C2 acid
Figure 1. Temperature, wind and ozone data during
sampling the period.
centrations of ozone. However, there could be substantial
differences in oxidation rates for reactions with hydroxyl
(maximum during the day) or nitrate (maximum at night)
radicals.
[15] The gaseous concentrations of the monoterpenes,
nopinone, pinonaldehyde, pinonic acid, benzene and methacrolein/methyl vinyl ketone/crotonaldehyde were determined alongside the particulate air sampler. The results
showed that the monoterpene concentrations were approximately twice as high at night (6000 ± 2400 ng/m3 or 1.2 ±
0.52 ppbv; n = 50) compared to the day (3200 ± 1100 ng/m3
or 0.67 ± 0.24 ppbv; n = 36) (Figure 2). The higher
nighttime concentrations of the monoterpenes, which is
due to more vertical stability and slower oxidation rates
compared to daytime conditions, is regularly observed at
Blodgett Forest [Holzinger et al., 2005]. The gas-phase
concentrations of nopinone were not significantly different
between day (570 ± 200 ng/m3 or 0.12 ± 0.042 ppb; n = 36)
and night (580 ± 180 ng/m3 or 0.12 ± 0.036 ppb; n = 50)
during the sampling period. The lack of a strong diurnal
cycle in gas phase concentrations of nopinone was also
consistent with previous research at this site [Holzinger et
al., 2005]. Gaseous pinonaldehyde was sporadically
detected with concentrations near the detection limit. When
Figure 2. Gaseous concentrations of monoterpenes, nopinone and benzene as determined by PTR-MS during the
course of particulate sampling period. The line represents
the 10-point moving average of the data.
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CAHILL ET AL.: SOA FORMED FROM OXIDIZATION OF BVOCS
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Table 1. Recovery of Typical Analytes From High-Volume Air
Filters as Determined by Three Spike Recovery Trials and
Surrogate Compounds Added to Field Samples
Compound
Spike-recovery trials (n = 3)
Acids
Pentanoic acid
Decanoic acid
Pentadecanoic acid
Pentanedioic acid
Cis-pinonic acid
Pinic acid (two isomers)
Trans-norpinic acid
Carbonyls
2-decanone
2-indanone
Nonanal
3,5-heptanedione
Pinonaldehyde
Field sample surrogates (n = 11)
Dodecanoic-13C1 acid
Butanedioic-13C2 acid
Glucose-d2
6-fluorochromanone
Pinonaldehyde-13C1
Average Recovery, %
SD (n = 3)
0.0
150
110
77
97
81
72
0.0
26
7.5
11
9.0
9.5
5.2
71
28
84
35
80
14
17
17
8.3
15
134
64
75
110
see notea
30
17
17
7.4
a
Large amounts of native pinonaldehyde in the samples contributed
significant fraction amount of pinonaldehyde-13C1, thus obscuring the
labeled compound added to the samples.
recovery from the field samples averaged 64%, which was
still reasonable but lower than expected on the basis of the
pentanedioic acid spike recovery results. Dodecanoic-13C1
acid recovery from field samples was high at 134 ± 30%,
which may be partly due to natural 13C isotopes present in
native dodecanoic acid. For the carbonyls, 2-indanone and
3,5-heptanedione showed poor recoveries with 28 and 35%
for reasons that are not completely clear. Overall, the
recovery of terpene oxidation products, such as cis-pinonic
acid (97 ± 9.0%), pinic acid (81 ± 9.5%), trans-norpinic
acid (72 ± 5.2%) and pinonaldehyde (80 ± 15%), were good
for this extraction procedure. These extraction experiments
were used to validate the efficiency of the extraction
methodology, but we did not use the spike-recovery data
to correct the observed field concentrations.
[17] The field blanks proved the substrates were largely
free of the analytes of interest or were at low enough
concentrations as not to interfere with field sample quantification. For the acids, the exceptions were benzoic (MQL =
2.1 ng/m3), hexanoic (MQL = 5.2 ng/m3), heptanoic
(MQL = 0.57 ng/m3) and oxalic acids, where oxalic acid
suffered from instrumental problems. For the sugars, the
exceptions were sucrose (MQL = 1.7 ng/m3) and inositol
(MQL = 0.40 ng/m3), which interfered with the quantification of 7 and 9 of the 11 samples, respectively. For the
carbonyls where quantification was attempted, only decanal
(MQL = 0.97 ng/m3) showed an appreciable contamination
in the field blank, although some of the lighter carbonyls,
such as acetone, also showed significant blanks, but these
volatile species were not quantified since they were not
expected to be retained on a filter. Unfortunately, most of
the targeted n-alkanes from C20 to C36 showed enough
contamination in the field blanks to make the quantification
unreliable, so only three n-alkanes were quantified.
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3.3. Acid Results
[18] The first class of chemicals investigated for biogenic
contributions to aerosols were the acids since these compounds were expected to condense onto aerosol because of
their lower vapor pressures (Table 2). Overall, the sum
of 24 detected acid species ranged from 150 to 510 ng/m3
with hexadecanoic (avg = 94 ng/m3), octadecanoic (avg =
22 ng/m3), cis-pinonic (avg = 14 ng/m3) and glyoxylic
(avg = 13 ng/m3) being the most abundant acids. Cis-pinonic
and pinic acids are products of ozone oxidation of a-pinene,
b-pinene, and in the case of pinic acid, sabinene and D3carene as well [Yu et al., 1999], thus these chemicals have a
oxidized BVOC source. We did not detect trans-norpinic
acid. However, Yu et al. [1999] observed that norpinic acid
had a low product yield as a result of ozone interacting with
a-pinene and b-pinene, so the lack of detection in the field is
likely correct and indicative of low concentrations. The
concentrations of the terpene-derived acids detected in
Blodgett Forest were comparable to other studies (Table 3)
[Kavouras et al., 1998, 1999b; Kavouras and Stephanou,
2002a, 2002b; Spanke et al., 2001]. The alkanoic acid series
from decanoic to nonadecanoic acids showed that the even
carbon numbered acids were far more prevalent than the odd
carbon number acids (Table 2). The general trend of the
alkanoic acids and alkanedioic acids were similar to those
observed by Yue and Fraser [2004] around Houston, Texas,
except that hexadecanoic acid was the predominant acid in
this study while octadecanoic acid was the most abundant
acid in their research.
[19] The acids results showed significant day/night differences in concentrations of cis-pinonic (Figure 3), pinic and
octadecanoic acids (Table 2). In the case of pinic and cispinonic acids, the concentrations were significantly higher at
night while octadecanoic acid was higher during the day (ttest, P < 0.05). The gas-phase monoterpene data showed
approximately two-fold higher monoterpene concentrations
at night. In addition, the nighttime samples would experience
cooler sampling conditions that should shift the gas-particle
partitioning of the chemicals into the particulate phase
[Janson et al., 2001; Kavouras et al., 1999a] or allow gasphase chemicals to adhere and be more retained by the filter.
Thus the higher concentrations of the terpene-derived acids
at night were reasonable.
3.4. Carbonyl Results
[20] The second class of chemicals that may have biogenic sources through oxidation of terpenes are the carbonyls. Only 19 of the 113 carbonyls in our calibration
standards were detected in the filter samples. Furthermore,
the majority of the detected compounds were volatile
species (e.g., acetone, methacrolein, methyl vinyl ketone,
etc.) that were unlikely to be retained on a filter for any
extended period of time during sampling. If only the less
volatile species are considered, then the number of detected
compounds drops to 7 (Table 4), of which most are still
semivolatile (e.g., nopinone) and were more abundant in the
gas phase than the particulate phase [Yu et al., 1999].
Therefore the particulate numbers are likely to be underestimates because of blow-off. The most abundant carbonyl
species detected were pinonaldehyde with an average concentration of 110 ng/m3 followed by methylglyoxal (avg =
29 ng/m3), glyoxal (avg = 9.5 ng/m3) and nopinone (avg =
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Table 2. Concentrations of Acids (ng/m3) Collected on High-Volume Quartz Filtersa
Compound
Overall Average ± SD
Daytime Average ± SD
Nighttime Average ± SD
t-Test (P-Value)
Decanoic acid
Undecanoic acid
Dodecanoic acid
Tridecanoic acid
Tetradecanoic acid
Pentadecanoic acid
Hexadecanoic acid
Heptadecanoic acid
Octadecanoic acid
Nonadecanoic acid
9-octadecenoic acid
Oxalic acid
Propanedioic acid
Butanedioic acid
Pentanedioic acid
Hexanedioic acid
Heptanedioic acid
Octanedioic acid
Nonanedioic acid
Decanedioic acid
L-tartaric acid
DL-malic acid
Fumaric acid
Maleic acid
Glycolic acid
Citraconic acid
Glyoxylic acid
Benzoic acid
Cis-pinonic acid
Pinic acid
Trans-norpinic acid
2-Methylglyceric acidd
0.7 ± 0.4
1.7 ± 1.2
9.8 ± 7.8
1.7 ± 0.8
7.9 ± 3.1
2.1 ± 0.7
94 ± 100
1.5 ± 1.0
22 ± 14
4.5 ± 2.6
6.9 ± 2.8
see noteb
see notec
5.5 ± 1.6
5.8 ± 9.7
see notec
0.7 ± 0.4
see notec
5.8 ± 2.1
3.5 ± 2.4
see notec
2.2 ± 2.2
0.4 ± 0.2
see noteb
2.1 ± 2.0
see notec
13 ± 5.6
see notec
14 ± 14
4.3 ± 3.0
see noteb
8.2 ± 7.4
0.8 ± 0.3
1.4 ± 1.0
7.0 ± 8.5
1.3 ± 0.5
7.8 ± 3.6
2.3 ± 0.8
140 ± 120
1.7 ± 1.2
31 ± 14
5.0 ± 1.6
7.4 ± 2.3
see noteb
see notec
5.9 ± 2.0
6.1 ± 11
see notec
0.7 ± 0.4
see notec
5.8 ± 2.9
3.8 ± 3.0
see notec
2.6 ± 2.9
0.3 ± 0.2
see noteb
2.7 ± 2.5
see notec
17 ± 5.3
see notec
3.6 ± 0.7
2.2 ± 0.5
see noteb
11 ± 8.6
0.6 ± 0.5
2.0 ± 1.4
13 ± 6.0
2.2 ± 0.7
8.1 ± 2.7
1.8 ± 0.4
39 ± 22
1.2 ± 0.6
12 ± 4.5
3.9 ± 3.5
6.5 ± 3.4
see noteb
see notec
5.0 ± 0.8
5.4 ± 9.0
see notec
0.7 ± 0.4
see notec
5.8 ± 0.7
3.1 ± 1.7
see notec
1.8 ± 1.1
0.5 ± 0.1
see noteb
1.3 ± 0.8
see notec
10 ± 4.2
see noteb
27 ± 9.1
6.9 ± 2.7
see noteb
4.7 ± 4.2
NS
NS
NS
0.036
NS
NS
NS
NS
0.019
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
0.004
0.018
NS
a
The average concentration (±SD) is given for all the samples, then only the daytime samples (n = 6), and then only the nighttime samples (n = 5). A
Student’s t-test (P < 0.05) was used to test for differences in concentrations between the day and night samples where ‘‘NS’’ indicates that the compound
was not statistically different.
b
This compound was not detected in any samples either because of low concentrations or high field blank values.
c
This compound was below the limit of quantification in more than half of the samples, so no average was calculated.
d
No standard was available, hence quantification was conducted assuming similar instrumental response similar to glycolic acid. Identification was based
on a match of the mass spectrum with spectrum presented by Edney et al. [2005] and similar retention time.
2.8 ng/m3). The concentration of pinonaldehyde in the gas
and particulate phases were comparable while the particulate concentration of nopinone was considerably lower than
the average gas phase with concentrations of 670 ng/m3.
Furthermore, Kavouras et al. [Kavouras et al., 1999a;
Kavouras and Stephanou, 2002a] also showed higher gas
phase concentrations of pinonaldehyde and nopinine in field
samples. Both nopinone and pinonaldehyde are products
from the ozone degradation of b-pinene. [Yu et al., 1999]
showed that pinonaldehyde was the most abundant species
Table 3. Concentrations of Particulate Monoterpene Oxidation Products (ng/m3) Observed in This Study Compared to Previous
Researcha
Day
Average ± SD
Night
Average ± SD
Overall
Range
Cis-pinonic acid
3.6 ± 0.7
27 ± 9.1
2.6 – 37
Pinic acid
(sum of two isomers)
Trans-norpinic acid
2.2 ± 0.5
6.9 ± 2.7
1.7 – 10
not detectedg
not detectedg
Nopinone
1.8 ± 0.4
3.9 ± 1.0
1.5 – 5.1
Pinonaldehyde
35 ± 34
200 ± 98
16 – 320
Compound
Literature
0.2 – 71.0,b 1.0 – 25.7,c 7.05 – 97.74,d 9.68 ± 7.96 (0600 – 1200 LT),e
1.16 ± 0.91 (1200 – 1800 LT),e 3.99 ± 2.45 (1800 – 0600 LT)e
0.8 – 4.0,f 0.4 – 4.4,c 0.39 – 82.72,d 2.38 ± 1.53 (0600 – 1200 LT),e
0.49 ± 0.46 (1200 – 1800 LT),e 3.03 ± 1.55 (1800 – 0600 LT)e
0.4 – 5.4,c 0.0 – 13.85,d 1.94 ± 1.92 (0600 – 1200 LT),e
0.14 ± 0.09 (1200 – 1800),e 1.51 ± 1.02 (1800 – 0600 LT)e
0.2 – 13.0,b 0.1 – 0.5,c 0.0 – 13.24,d 0.17 ± 0.22 (0600 – 1200 LT),e
0.02 ± 0.03 (1200 – 1800),e 0.24 ± 0.14 (1800 – 0600 LT)e
0.2 – 32.0,b 0.3 – 1.2,c 0.17 – 32.12,d 0.78 ± 0.54 (0600 – 1200 LT),e
0.27 ± 0.28 (1200 – 1800),e 0.88 ± 0.50 (1800 – 0600 LT)e
a
The concentrations are divided into day (n = 6) and night (n = 5) samples since the concentrations between these two time classes are significantly
different.
b
Kavouras and Stephanou [2002b].
c
Kavouras et al. [1998]. The nopinone and pinonaldehyde values were estimated from a figure.
d
Kavouras et al. [1999b].
e
Kavouras and Stephanou [2002a]. Values are presented for different times of the day.
f
Sum of pinic and pinonic acids presented by Spanke et al. [2001].
g
The limit of quantification was 0.48 ng/m3.
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Figure 4. Correlation (R2 = 0.79) between the ambient
temperature expressed as inverse temperature in Kelvin and
the natural logarithm of gas-particulate ratio of nopinone.
The Clausius-Clayperon plot predicts the DHvap for
nopinone to be 55.4 KJ/mol. The results showed that
increasing temperature caused a shift of the partitioning of
nopinone to the gas phase.
Figure 3. Diurnal cycle in particulate concentrations of
the monoterpene oxidation products of pinonic acid,
pinonaldehyde and nopinone.
formed in their laboratory experiments, which was consistent with the field measurements. The observed pinonaldehyde concentrations in this study were considerably higher
than cis-pinonic and pinic acid concentrations, which is also
in agreement with the laboratory experiments [Yu et al.,
1999]. While the concentrations of particulate-associated
nopinone observed in this study were consistent with
previous research, the concentrations of pinonaldehyde
were considerably higher (Table 3) [Kavouras et al.,
1998, 1999b; Kavouras and Stephanou, 2002a, 2002b].
Table 4. Concentrations of Carbonyls (ng/m3) Collected on HighVolume Quartz Filtersa
Compound
Glyoxal
Methylglyoxal
Decanal
2,5-hexanedione
Benzophenone
Nopinone
Pinonaldehyde
Overall
Daytime
Nighttime
t-Test
Average ± SD Average ± SD Average ± SD (P-Value)
9.5 ± 6.5
29 ± 10
1.3 ± 0.8
0.5 ± 0.1
0.2 ± 0.3
2.8 ± 1.3
110 ± 110
11 ± 7.3
26 ± 5.2
1.6 ± 1.0
0.5 ± 0.2
0.1 ± 0.1
1.8 ± 0.4
35 ± 34
8.3 ± 5.8
33 ± 14
1.0 ± 0.3
0.4 ± 0.1
0.3 ± 0.4
3.9 ± 1.0
200 ± 98
NS
NS
NS
NS
NS
0.007
0.018
a
The average concentration (±SD) is given for all the samples, then only
the daytime sample, and then only the nighttime samples. A Student’s t-test
was used to test for differences in concentrations between the day and night
samples. Statistical differences were defined at the P < 0.05 level while
‘‘NS’’ indicates that the compound was not statistically different.
This may be the result of differences in vegetation types
between different study sites.
[21] The terpene-derived carbonyl oxidation products
(Table 4) showed a strong diurnal cycle in a similar fashion
as the terpene-derived acids (Figure 3). Pinonaldehyde
concentrations at night were approximately 5.7-fold higher
than the daytime concentrations while nopinone concentrations were 2.2-fold higher at night. Once again, the higher
concentrations of pinonaldehyde and nopinone in the aerosol at night were probably the result of slower vertical
mixing and heterogeneous-condensation from the gas phase
onto particles at cooler temperatures [Janson et al., 2001;
Kavouras and Stephanou, 2002a]. The gas to particulate
ratio for nopinone showed a strong dependence on ambient
temperature (Figure 4), with increasing partitioning of
nopinone into particulate matter as temperatures decreased.
The gas-particulate ratio varied with ambient temperature
following a Clausius-Clayperon relationship with an apparent DHvap for nopinone of 55.4 KJ/mol. This comparison
quantifies how important temperature is for heterogeneous
condensation of this terpene oxidation product onto aerosols, and provides the first measure of an effective enthalpy
of vaporization that can be used in atmospheric chemistry
models for nopinone.
3.5. Sugars and Sugar-Derived Compounds
[22] The results from the sugar analysis (Table 5) procedure showed that levoglucosan (avg = 16 ng/m3) was the
most abundant compound detected followed by glucose
(avg = 13 ng/m3). The sum of the sugar mass was 53 ±
15 ng/m3, which was comparable to the sugars observed in
the Howland Experimental Forest in Maine, USA, which is
also a predominately conifer-based forest [Medeiros et al.,
2006]. Levoglucosan is a known wood combustion product
[Simoneit et al., 1999; Simpson et al., 2004], which implies
that the site had wood smoke traces in the samples.
However, the concentrations were generally much lower
than those observed by other research groups such as Nolte
et al. [2002] at Kern Wildlife refuge (106 ng/m3); Bakers-
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Table 5. Average (±SD, n = 11) Concentrations (ng/m3) of Sugars
and Polyols Collected on High-Volume Quartz Filtersa
Compound
Sugars
Methyl-b-L-arabinopyranoside
D-arabitol
Levoglucosan
Fructose
Glucose
Mannitol
Inositol
Sucrose
Sum of sugars
Polyols
2-methylthreitol
2-methylerythritol
2-methylglyceric acidd
Pentenetriol (first eluting)e
Pentenetriol (second eluting)e
Pentenetriol (third eluting)e
Sum of polyols
Overall Average ± SD
see noteb
7.6 ± 2.6
16 ± 13
4.9 ± 2.5
13 ± 5.2
8.8 ± 3.4
see notec
see notec
53 ± 15
13 ± 3.8
32 ± 9.6
8.2 ± 7.4
1.1 ± 0.43
0.47 ± 0.11
1.9 ± 1.1
57 ± 19
a
No statistical differences (t-test, P > 0.05) were observed between day
and night samples, so only an overall concentration is presented for the
study.
b
This compound was not detected in any samples.
c
This compound was below the limit of quantification in more than half
of the samples, so no average is calculated.
d
No standard was available, so quantification was based on assuming a
similar instrumental response as glycolic acid.
e
No standard was available, so quantification was based on assuming a
similar instrumental response as 2-methylerythritol.
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and boreal forests in Finland [Kourtchev et al., 2005]. The
two 2-methyltetrols combined had an average mass loading
of 45 ± 13 ng/m3, which is comparable to concentrations
observed in the Amazon, Hungary and Finland in the
summer [Claeys et al., 2004a; Ion et al., 2005; Kourtchev
et al., 2005]. This confirms the ubiquitous nature of the
2-methyltetrols in SOA from vastly different geographic
regions.
[24] Although the 2-methyltetrols were expected to be
photochemically produced, they did not show clear diurnal
cycles (Figure 5) like some of the monoterpene oxidation
products such as pinonic acid and pinonaldehyde (Figure 3).
This contrasts with Ion et al. [2005] who observed a clear
diurnal cycle with higher concentrations during the day.
These differences are likely due to different meteorology at
the sites. A comparison between the 2-methyltetrols and the
other chemicals showed that tetrols correlated best with
ozone followed by glucose and fructose (Figure 6). It is
intriguing that the 2-methyltetrols correlated well with the
sugars. It may be possible that the 2-methyltetrols may arise
directly from a similar biological source as the sugars in
addition to the well documented photochemical pathway.
The poor correlation with the monoterpene oxidation products (Figure 6) would suggest the 2-methyltetrols would
have a different source, but the strong correlation with
ozone and other highly oxidized compounds, such as
glyoxal, supports an atmospheric oxidation formation path-
field (2390 ng/m3); Fresno (2980 ng/m3); Pashynska et al.
[2002] in Ghent, Belgium (summer avg = 19.1 and winter
avg = 420 ng/m3); Yue and Fraser [2004] around Houston
before and after a smoke episode (20.8 to 663.4 ng/m3); and
Simpson et al. [2004] in the Seattle area (10 to 760 ng/m3).
Considering the site is located in the Sierra Nevada Mountains where wood fire places are frequently used for heating
homes and fire is often used to dispose of logging debris, it
is reasonable to expect periodic observations of levoglucosan. Unlike the terpene oxidation products, the sugars and
sugar derivatives did not show clear diurnal cycles
(Figure 5). The source of these chemicals was probably
primary particulate emissions since these chemicals are
essentially nonvolatile and not present in the ambient gas
phase of the atmosphere. It should be noted that the sampler
used was a total suspended particulate sampler that would
collect coarse biological particulate matter, such as plant
detritus, microbes and spores, that may contain sugars
[Medeiros et al., 2006].
3.6. Polyols
[23] The polyols, namely the 2-methyltetrols, 2-methylglyceric acid and pentenetriols (i.e., cis and trans 2-methyl-1,3,
4-trihydroxy-1-butene and 3-methyl-2,3,4-trihydroxy-1butene), have recently been recently identified as marker
compounds for photo-oxidation of isoprene in aerosols
[Claeys et al., 2004a; Edney et al., 2005; Wang et al.,
2005]. Two diastereoisomeric 2-methyltetrols were detected
in the samples with 2-methylerythritol being about 2.5-fold
more abundant than 2-methylthreitol (Table 5), which is a
similar ratio as observed in other studies in the Amazon
[Claeys et al., 2004a], K-puszta, Hungary [Ion et al., 2005]
Figure 5. Day and night particulate concentrations of
levoglucosan, glucose and 2-methylerythritol.
8 of 14
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Figure 6. Correlation between several different chemicals and 2-methylerythritol. The strongest
correlations observed were with ozone, glucose and fructose.
way. The weak correlation between PTR-MS results for ion
[71]+, which corresponds to methacrolein, methyl vinyl
ketone and crotonaldehyde, and the 2-methyltetrols was
expected since both methacrolein and methyl vinyl ketone
are also oxidation products of isoprene and have been
shown to correlate with ozone production from isoprene
oxidation at this site [Dreyfus et al., 2002]. The lack of
correlation between the pinene-derived compounds and the
isoprene-derived compounds results from the regional biomass distribution and meteorology. Isoprene emission in the
region is dominated by oak trees found at lower elevations
and transported upslope in rising air masses during the
daytime. The monoterpene emissions occur mainly from the
local mixed conifer forests and have maximum concentrations when atmospheric mixing is the lowest, so their
maximum concentrations occur at night.
[25] In addition to the 2-methyltetrols, 2-methylglyceric
acid (2,3-dihydroxypropanoic acid), which is an oxidation
product of methacrolein, a first-generation photo-oxidation
product of isoprene [Claeys et al., 2004b] was also observed. The 2-methylglyceric acid correlated well with
glycolic acid (R2 = 0.72), ozone (R2 = 0.54), 2-methyltetrols
(R2 = 0.38) and glucose (R2 = 0.32). The strong correlation
with glycolic acid (or hydroxyacetic acid) was unexpected,
but the correlation is intriguing since these two chemicals
are structurally very similar in that they differ by only a CHOH group. This implies that these compounds may have
similar sources. Overall, the 2-methylglyceric acid was
approximately four-fold more abundant than glycolic acid,
although quantification is tenuous since we lacked a standard for 2-methylglyceric acid and assumed it had the same
relative response as glycolic acid.
[26] The last group of polyols investigated were the
pentenetriols, of which three were detected (Table 5).
Standards were not available for these compounds, so
identification was based on the mass spectra and relative
retention time presented by Wang et al. [2005]. Quantification was conducted assuming a similar relative response to
the 2-methyltetrols. Although the pentenetriols are believed
to be related to the 2-methyltetrols [Wang et al., 2005], the
pentenetriols and 2-methyltetrols showed a relatively weak
correlation (R2 = 0.16). Moreover, the pentenetriols did not
correlate well with any other chemical but the strongest
correlations were observed with levoglucosan (R2 = 0.40),
glycolic acid (R2 = 0.20), particulate methylglyoxal (R2 =
0.19) and the 2-methyltetrols (R2 = 0.16). The pentenetriols
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Table 6. Concentrations of n-Alkanes and Butyl Esters (ng/m3) Collected on High-Volume Quartz Filtersa
Compound
n-alkanes
C25 n-alkane
C26 n-alkane
C27 n-alkane
C28 n-alkane
C29 n-alkane
C30 n-alkane
C31 n-alkane
C32 n-alkane
C33 n-alkane
C34 n-alkane
C35 n-alkane
C36 n-alkane
Sum of quantified alkanesd
Upper limit of alkane masse
Butyl esters
Hexadecanoic acid, butyl ester
Octadecanoic acid, butyl ester
Overall Average ± SD
Daytime Average ± SD
Nighttime Average ± SD
t-Test (P-Value)
LOQ, ng/m3
0.23
0.27
1.82
4.67
6.00
2.89
0.61
0.13
0.80
0.15
0.15
0.15
2.0 ± 1.4
1.4 ± 1.3
see noteb
see noteb
see noteb
see noteb
1.0 ± 1.2
see noteb
see noteb
see noteb
see noteb
see notec
7.2 ± 8.3
22 ± 4.9
2.9 ± 1.3
2.1 ± 1.3
see noteb
see noteb
see noteb
see noteb
1.3 ± 1.5
see noteb
see noteb
see noteb
see noteb
see notec
10 ± 10
0.87 ± 0.32
0.43 ± 0.38
see noteb
see noteb
see noteb
see noteb
0.69 ± 0.74
see noteb
see noteb
see noteb
see noteb
see notec
3.2 ± 2.8
0.014
0.02
NS
8400 ± 5100
5000 ± 5200
12000 ± 4200
8200 ± 5100
4400 ± 2500
1100 ± 750
0.007
0.018
NS
a
The average concentration (±SD) is given for all the samples, then only the daytime sample, and then only the nighttime samples. A Student’s t-test was
used to test for differences in concentrations between the day and night samples. Statistical differences were defined at the P < 0.05 level.
b
The compound was either not detected or below the limit of quantification, defined as the mean field blank + 6 SD of the blank, in more than half the
samples, so no average value is presented.
c
This compound was not detected in any samples.
d
The sum of quantified mass includes all chemical species above the LOQ including chemicals that were sporadically detected, hence this value is higher
than sum of the C25, C26, and C31 n-alkanes.
e
The upper limit of alkane mass was defined as the quantified chemicals plus the limit of quantification for the chemicals that fell below the LOQ.
did not correlate with ozone (R2 = 0.091) or temperature
(R2 = 0.0004) as was the case with the methyltetrols.
3.7. Alkanes and Butyl Esters
[27] The last class of chemicals investigated was the
‘‘native’’ hydrocarbons that did not require any derivatization. The target analytes were the C20 through C36
n-alkanes. Unfortunately, the field blanks showed sufficient
contamination to make quantification unreliable except for
the C25, C26, and C31 alkanes (Table 6). The C20 to C24
alkanes could not be quantified because of coelution with
the butyl ester series (see below) since the alkanes lacked a
strong, unique ion for quantification. Overall, the alkanes
contributed relatively little mass to the particulate matter
with a total quantified mass of 7.2 ± 8.3 ng/m3. The
maximum amount of alkanes that could have been present
was estimated to be 22 ± 4.9 ng/m3 by summing the
quantified compounds and the limit of quantification for
the chemicals that fell below the LOQ. Even this maximum
possible amount of alkanes was still relatively minor compared to the biogenic chemicals. Unlike the terpene-derived
compounds, the observed alkanes showed diurnal cycles
with higher concentrations during the day. This is most
likely the result of transport from the Sacramento urban
area, which was demonstrated by the increase in gaseous
benzene concentrations during the day.
[28] An unexpected observation was the presence of two
peaks that dominated the chromatograms. These two compounds were absent from the field blanks, indicating that
they were not the result of contamination from the substrate,
reagents, handling or storage of the samples. Both the
electron ionization and methane chemical ionization mass
spectra were used to identify the compounds as the butyl
ester of hexadecanoic acid (hereafter C16-butyl ester) and
the butyl ester of octadecanoic acid (C18-butyl ester).
Authentic standards were purchased from Chem Service
(West Chester, Pennsylvania) to confirm compound elution
time as well as the EI and CI mass spectra, which proved the
identity of these analytes. A third peak in the chromatogram
had a similar fragmentation pattern as the C16 butyl ester,
but it was smaller by 28 mass units (probably C2H4), so we
believe that it is the C14-butyl ester, but the lack of an
authentic standard prevents confirmation of this compound.
[29] The field samples were re-analyzed with a calibration
curve to quantify the C16 and C18 butyl esters. The results
showed very high concentrations of C16-butyl ester (avg =
8400 ng/m3) and the C18-butyl ester (avg = 5000 ng/m3)
(Table 6). These two compounds by themselves exceed the
extractable organic matter found in previous high-volume
air sampling conducted in the region [Simoneit, 1984] and
the total organic matter as determined by the Interagency
Monitoring of Protected Visual Environments (IMPROVE)
network at Bliss State Park (available at http://vista.cira.
colostate.edu/IMPROVE/). Therefore we consider these
values suspect. Furthermore, these two butyl esters should
have been observed in the previous extraction and analytical
procedures if they were present [Simoneit, 1984].
[30] While we were confident in the identification and
quantification of the butyl esters, questions remain regarding the potential origin of these two compounds that were
very abundant in our samples but not the field blanks.
Analysis of soil, Ponderosa pine needles and Ponderosa
pine wood from Blodgett Forest did not show any butyl
esters. The filter patches extracted by shaking the samples in
hexane rather than extracted by Soxhlet also showed high
concentrations of the butyl esters, which eliminates the
possible formation of the butyl esters during the extraction
procedure. Currently, we do not know the source of the C16
and C18 butyl esters, so additional experiments will be
necessary to determine if the butyl esters are an artifact
10 of 14
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Figure 7. Plot of the principal component analysis (PCA)
scores for samples collected during the study period. The
samples fell into two clusters, generally corresponding to
day and night samples, along the first PCA axis.
arising from other equipment at the sampling site or if they
are arising from another local source (e.g., pollen).
4. Principal Component Analysis
[31] The particulate data were used to investigate the
potential source of different chemical classes through correlations as previously shown. However, the large number
of potential correlations between the different chemicals
makes the data set more suited to multivariate statistical
analysis. Therefore we conducted a principal component
analysis (PCA, Minitab Statistical Software version 13.20)
where all chemical species that were detected in at least 6 of
the 11 samples were treated as variables along with the gasphase PTR-MS chemical results, ozone and temperature
(Figure 7). Typically PCA analyses are conducted on larger
data sets, so the limited number of samples (n = 11) makes
the current PCA analysis more tentative than studies with
larger sample sets. However, the results are useful in an
exploratory fashion and to support previous observations
from the regression analyses.
[32] The results showed that the samples formed two
groups along the first PCA axis based on diurnal differences, which was expected on the basis of meteorology
suspected sources of particulate chemicals. A single daytime sample fell into the nighttime grouping, but this sample
was taken on the first day when it was cool and cloudy and
therefore was not typical of the other daytime samples
(Figure 1).
[33] The PCA coefficients for the different chemicals and
conditions showed that diurnal differences were largely
responsible for most of the variation along the first PCA
axis (Figure 8) where chemicals with higher concentrations
at night had larger positive PCA coefficients while chemicals that where higher in the day had negative values along
PCA axis 1. The second PCA axis appeared to differentiate
between biogenic and anthropogenic chemicals, but the
separation is not as clear as the first PCA axis. Four
chemical groupings were observed in the plot of the PCA
variable coefficients although there were chemicals spread
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throughout the plot (Figure 8). The first small group
consisted of gaseous monoterpenes and the terpene oxidation products, although tridecanoic acid also fell into this
group and pinonaldehyde fell outside the group. The relatively tight cluster of the monoterpenes and terpene oxidation products away from the temperature and ozone
parameters suggest that the gaseous monoterpene concentrations were a more important parameter in controlling the
terpene oxidation product concentrations than ozone or
temperature. The terpene oxidation product abundance in
the aerosols were inversely related to air temperature as
shown for nopinone. The second cluster was very tight and
consisted of temperature, gaseous benzene, the butyl esters,
octadecanoic acid, the C25 and C26 n-alkanes. This group of
chemicals likely indicates transport of air pollution from the
Sacramento urban area, as evidenced by the gaseous benzene. The third grouping consists of a series of alkanoic and
alkanedioic acids that are relatively independent of diurnal
cycle. It is interesting that the majority of the alkanoic and
alkanedioic acids grouped together away from other chemicals. The last group is composed of ozone and a series of
oxidized chemicals as well as glucose and fructose. The
close proximity of the 2-methyltetrols to glucose and
fructose may be a statistical fluke, but it might also be
suggestive of a similar source for these chemicals. However,
the presence of ozone and some other highly oxidized
species (e.g., glyoxal) is consistent with oxidation being
an important source for this group of chemicals as has been
previously observed.
5. Discussion
[34] The objective of this research was to assess the
importance of oxidized biogenic emissions for organic
aerosol concentrations by collecting a series of highvolume particulate samples over 5 days and nights in
Blodgett Forest. The results clearly show that terpene and
isoprene oxidation products represent the more abundant
chemicals on the filters with the exceptions of the butyl
esters, whose source is not known, and the C16 and C18
alkanoic acids (Table 7). Pinonaldehyde was the most
abundant chemical quantified, excluding the butyl esters,
accounting for 22% of the identified organic aerosol mass
over the entire study, and 39% of the aerosol mass during
the nighttime samples. Cis-pinonic acid was the third
most abundant acid and represented 2.9% of the overall
quantified organic species mass while 2-methylglyceric
acid was the 6th most abundant acid. Overall, the terpene
oxidation products constituted 27% of the average identified aerosol organic constituents, excluding the two
butyl esters, while the isoprene oxidation products of
the 2-methyltetrols and 2-methylglyceric acid represented
11% identified aerosol mass during the study. In total, the
oxidation products of terpenes and isoprene accounted for
180 ng/m3 of organic aerosol during the course of the
study and 280 ng/m3 at night. This result clearly highlights the importance of oxidized BVOC sources for
secondary organic aerosols in this and similar forested
regions with strong BVOC emissions.
[35] The mass of identified organic material in this
study can be compared to total organic carbon measurements in the region conducted by the IMPROVE network
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Figure 8. Plot of the principal component analysis coefficients for the chemicals observed at Blodgett
Forest. In general, compounds that are higher during the daytime are on the left side of PCA axis 1 while
chemicals higher at night are on the right-hand side. It appears that recently biologically derived
compounds have low scores on the second PCA axis. Group 1 mostly consists of monoterpenes and
monoterpene derived chemicals. Group 2 contains gaseous benzene, which is a marker of transport from
urban areas, and the butyl esters and a couple alkanes. Group 3 consists solely of alkanoic acids,
alkanedioic acids, malic acid and arbitol. Group 4 contains ozone, glucose, fructose, the 2-methyltetrols
and 2-methylglyceric acid.
at Bliss State Park. The Bliss site is located approximately
46 km east of Blodgett Forest at an elevation 2100 m, but it
is representative of aerosols on the west side of the Sierra
Nevada Mountains [Cahill et al., 1996]. The organic carbon
data, as determined by combustion, from the IMPROVE
Bliss site in September 2002 was 1.4 ± 0.78 mg/m3 in 2002
(http://vista.cira.colostate.edu/IMPROVE/), which approximately corresponds to 2.1 ± 1.2 mg/m3 of organic matter
(Table 7). The Bliss site was impacted by forest fires in
September 2003, hence that data cannot be used for comparisons. If one assumes the sites are sampling a similar air
mass and that there is little difference between our TSP
aerosol collection and the IMPROVE PM10 collection with
respect to semivolatile organics, then approximately 23% of
the total organic matter was identified during this study and
BVOC oxidation products accounted for approximately
8.6% of the total organic material.
[36] It should be noted that quartz filters are subject to
both positive artifacts, resulting from the adsorption of
gases [Kirchstetter et al., 2001; Turpin et al., 1994], and
negative artifacts due to volatilization of semivolatile chemicals [Subramanian et al., 2004]. Since the IMPROVE
network also used quartz filters for the organic carbon
determinations, the data sets should be relatively comparable. The three main differences are (1) the high-volume
sampler employed in this study was a TSP sampler compared to the IMPROVE PM10 sampler, (2) the samples
collected herein were 11.5 h samples instead of 24 hour
samples and (3) the face velocity of the air through the filter
was 16.2 cm/s in this study compared to approximately
80 cm/s in the IMPROVE samplers. The difference in size
fractions collected may result in more organic matter being
collected in the PM10 to PM30 faction while the second
and third differences in sampling approaches would result in
less loss of semivolatile organics from the entrained particulate matter since there is less air passing by the collected
aerosols for a shorter period of time. However, the shorter
sampling period may give a greater bias because of the
adsorption of F gases to the filters since the filter may not be
at equilibrium with the incoming air stream [Subramanian
et al., 2004; Turpin et al., 1994].
[37] Another consideration in assessing the potential
contribution of oxidized biogenic chemicals to SOA formation is the potential for organic acids to form oligomers.
[Gao et al., 2004] estimated that 50% of the SOA mass
from a-pinene ozonolysis was oligomers consisting mainly
of dimers, although up to pentamers were observed. If
polymerization of biogenic oxidation products is also significant in our samples, then the contribution of oxidized
biogenic compounds would have an even greater contribu-
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Table 7. Summary of the Composition of the Total Identified and Quantified Organic Mass (Excluding the Butyl Esters Whose Source
Was Unknown) Represented by Different Chemical Classes or Specific Compounds
Sum of identified organics, ng/m3
Percent acids
Percent sugars
Percent carbonyls
Percent polyols
Percent alkanes
pinonaldehyde
nopinone
cis-pinonic acid
pinic acids (two isomers)
2-methyltetrols + 2-methylglyceric acid
Sum of biogenic oxidation products, ng/m3
Sum of biogenic oxidation products, %
Organic carbon at Bliss, Sep 2002,a mg/m3
Estimated organic matter,b mg/m3
Approximate % of organic matter identified
Approximate % contribution of oxidized BVOCs to organic matter
Overall
Daytime (0830 to 2030 LT)
Nighttime (2200 to 0800 LT)
490 ± 93
46.0
11.0
30.9
12.1
1.5
110 ± 110
2.8 ± 1.3
14 ± 14
4.3 ± 3.0
53 ± 19
180 ± 110
36.9%
1.4 ± 0.78
2.1 ± 1.2
23%
8.6%
480 ± 120
56.3
11.4
17.7
14.5
2.3
35 ± 34
1.8 ± 0.4
3.6 ± 0.7
2.2 ± 0.5
61 ± 20
100 ± 33
23.3%
520 ± 57
33.6
10.5
46.6
9.3
0.6
200 ± 100
3.9 ± 1.0
27.3 ± 9.1
6.9 ± 2.7
44 ± 13
280 ± 90
53.2%
a
PM10 carbon measurements determine by the IMPROVE network at Bliss State Park (http://vista.cira.colostate.edu/IMPROVE/). Organic carbon was
determined by combustion and elemental carbon by light adsorption. Data from 2002 were used instead of 2003 since a forest fire impacted the Bliss site
during September 2003, so the organic carbon number would not be representative.
b
Organic matter was estimated as 1.5 (Organic carbon) as suggested by Wolff et al. [1991].
tion to SOA than was determined from the single acids
species determined in this research.
[38] The higher concentrations of terpene oxidation products at night can be explained by the following three factors:
(1) lower vertical mixing rates at night cause higher concentrations of terpene-precursors despite reduced emission
due to lower temperature; (2) lower temperatures cause
increased condensation of the semivolatile organic species
onto aerosols; and (3) changing oxidant concentrations;
while ozone measurements confirm similar levels during
day and night, OH levels should be much lower at night,
and nitrate may contribute additional oxidation capacity
during night. The results from the gas-phase characterization of the site showed that the monoterpene concentrations
were approximately two-fold higher at night, which would
indicate that the oxidation products should also be about
two-fold higher at night if factors 2 and 3 above are
insignificant. The close proximity of the gaseous monoterpenes to the oxidation products in the PCA analysis
certainly suggests a strong relationship between the two
groups of chemicals. However, the nighttime concentration
of nopinone, pinonaldehyde, cis-pinonic acid and pinic acid
were 2.1, 5.7, 7.5 and 3.1-fold higher than the daytime
concentrations, respectively. An investigation of the nopinone concentrations in the gas phase and the particulate
phase clearly shows the effect of temperature on gas/particle
partitioning as shown by Figure 4 (factor 2), providing a
useful measure of the apparent enthalpy of vaporization
which can be applied in atmospheric chemistry modeling of
secondary aerosols. Therefore we conclude that the higher
particulate concentrations at night are the result of multiple
processes.
[39] The clear implication of these findings is that oxidized biogenic emissions contribute a significant fraction of
organic matter to ambient aerosols, thus these terpene and
isoprene oxidation products need to be considered as
important components of aerosols even at this site which
is 75 km downwind of Sacramento, a major urban area.
[40] Acknowledgments. First of all, we would like to give our
gratitude to Judi Charles for initiating and supporting this research.
Regrettably, Judi passed away during this project after a long fight against
cancer. We would like to thank the staff of the Blodgett Forest Research
Station for their assistance during this project and the DELTA Group and
the University of California for the use of their high-volume samplers. In
addition, we would like to thank Magda Claeys for the methyltetrol
standards and valuable insight into the data as well as Megan McKay for
the meteorology data collected at the site. This research was supported in
part by the National Science Foundation grant ATM 0003137 to UC Davis
and grant ATM 0443448 to UC Berkeley.
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